Batching
Processing multiple requests or data points together in a single operation rather than one at a time. This improves throughput and efficiency in AI systems.
In-Depth Explanation
Batching is the practice of grouping multiple items together for processing in a single operation, rather than handling each individually. This fundamental optimization technique can dramatically improve AI system efficiency.
Why batching matters:
- Cost reduction: API calls often have per-request overhead
- Throughput increase: More items processed per unit time
- Resource efficiency: Better GPU/CPU utilization
- Rate limit management: Fewer requests within limits
Types of batching:
- Request batching: Multiple inputs in one API call
- Embedding batching: Generate multiple embeddings together
- Inference batching: Process multiple prompts simultaneously
- Database batching: Bulk inserts/queries
Batching strategies:
- Fixed size: Wait for N items, then process
- Time-based: Process accumulated items every X seconds
- Hybrid: Whichever threshold is reached first
- Dynamic: Adjust batch size based on load
Trade-offs to consider:
- Larger batches = better efficiency but higher latency
- Smaller batches = lower latency but more overhead
- Memory constraints limit maximum batch size
Business Context
Batching can reduce API costs by 50-80% for US businesses processing high volumes. American companies running large-scale document analysis or embedding generation save significantly with optimized batch strategies.
How Clever Ops Uses This
We implement intelligent batching strategies for American businesses, optimizing the balance between cost savings and response time for each use case across US operations.
Example Use Case
"Processing 100 customer emails in a single batch rather than making 100 separate API calls, reducing costs and total processing time."